Postdoctoral Research Associate: Genetic and Molecular Epidemiology, Department of Genome Sciences

The Position

The Yang Lab (PI: Yaohua Yang, PhD) in the Department of Genome Sciences at the University of Virginia School of Medicine is recruiting a Postdoctoral Research Associate in genetic and molecular epidemiology. The lab identifies genetic and molecular determinants of cancer risk and prognosis and investigates how interactions between the commensal microbiome and the host shape cancer development, through integrative analysis of multi-level omics data (lab page).

Primary Project

The position is supported by an NCI R37 (MERIT) award investigating N6-methyladenosine (m6A) RNA modification in lung cancer, integrating epitranscriptomic profiling of human lung tissues with population-scale genetic and multi-omics data and functional validation. The project spans discovery, mechanism, and translation, and offers an unusually complete training arc for someone who wants to work at the interface of population genomics and cancer biology.

The postdoc will be co-mentored by Dr. Kexin Xu, Professor of Genome Sciences, whose laboratory specializes in m6A biology in cancer, and will have the opportunity to receive training from Dr. Jianjun Chen, Chair of Systems Biology at the Beckman Research Institute of City of Hope, a world-leading m6A biologist. This paired mentorship gives the postdoc direct access to expertise in both population-level genomics and cancer epitranscriptomic biology. A candidate trained on either side of that divide will have a genuine home in the other.

Broader Research Program

This is a lab, not a single project. Beyond the primary aim, several funded and developing directions are open to the postdoc to contribute to or lead, according to their own interests and ambitions:

• Integrating genetic with bulk and single-cell multi-omics data to identify biomarkers for complex diseases
• Investigating the impact of the commensal microbiome on the host epigenome and transcriptome
• Multi-omics analysis of the lower airway microbiome in lung cancer prognosis
• Proteogenomic approaches to identifying causal proteins and repurposable drugs for chronic lung disease

The lab actively supports postdocs in developing independent research directions and in applying for intramural and extramural funding, including NIH K awards, with the explicit goal of building a competitive record for the faculty job market.

Training

The successful candidate will receive comprehensive, individualized training in genetic and molecular epidemiology, statistical genetics, bioinformatics, and computational biology, complemented by co-mentorship in cancer molecular biology. Training is tailored to the candidate''s starting point rather than assumed.

The lab is supported by NCI K99/R00 and R37 (MERIT) awards and UVA startup funds.

Research Computing and AI Resources

The lab treats large language models and AI coding agents as standard research infrastructure, on the same footing as the compute cluster or a statistical package. Fluency with these tools is part of the training you will receive here, and it is a skill the lab expects its members to carry into their independent careers. The lab covers subscriptions to frontier AI models and coding agents, including Claude, ChatGPT, and Gemini, at the highest usage tiers available, so that no one in the lab rations their own research against a token limit. Use of these tools in the lab follows University and NIH data use requirements.

Lab members also have access to the University of Virginia''s high-performance computing (HPC) environment, dedicated storage, and the computational resources these workflows depend on.

Minimum Qualifications

• PhD, MD, or equivalent degree in epidemiology, genetics, genomics, biostatistics, bioinformatics, molecular biology, cell biology, or a related field, awarded or expected before the start date.

Research training in one of the following two tracks is highly preferred:

• Quantitative track: experience analyzing next-generation sequencing data and/or population-based cohort data, with solid programming skills in R and/or Python.
• Laboratory track: hands-on experience with DNA/RNA/protein extraction, sequencing or mass spectrometry sample preparation and library construction, and CRISPR-based genome editing, together with clear motivation to be trained in genetic epidemiology, bioinformatics, and computational biology. Candidates from this track are actively encouraged to apply; the analytical training is part of the position, not a prerequisite for it.

Preferred Qualifications

• At least one first-author peer-reviewed publication from doctoral research (published, accepted, or under review).
• Experience with statistical genetics methods such as genome-wide, transcriptome-wide, and proteome-wide association studies (GWAS, TWAS, PWAS), quantitative trait loci (QTL) mapping, Mendelian randomization, colocalization, or fine-mapping.
• Experience with genomic, epitranscriptomic, and/or epigenomic data, such as whole-genome sequencing (WGS), genotyping, RNA-seq, m6A-seq/MeRIP-seq, DNA methylation arrays, ATAC-seq, or single-cell assays.
• Prior experience with microbiome data analysis.
• Familiarity with machine learning or deep learning approaches applied to biological data.
• Demonstrated fluency with AI assistants and coding agents in a research setting, for example building or debugging analysis pipelines with them, or using them systematically to explore and benchmark analytical methods.
• Strong written and oral communication skills, and the ability to work effectively in a collaborative, interdisciplinary team.

Physical Demands

This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs.

Salary follows the NIH NRSA stipend scale for postdoctoral trainees, which for FY 2026 ranges from $63,480 to $77,076 depending on years of prior postdoctoral experience (NOT-OD-26-044), and is commensurate with experience.

This is an exempt-level, benefited position. Learn more about UVA benefits.

This is a restricted position, which is dependent on funding and is contingent upon funding availability.

The initial appointment is for one year, renewable annually based on performance and funding availability.

This position is based in Charlottesville, VA, and must be performed fully on-site.

To learn more about UVA and in the Charlottesville area, visit UVA Life and Embark CVA.

Application review will begin after August 31, 2026.

Background checks and pre-employment health screenings will be conducted on all new hires prior to employment.

How to Apply

Please apply online, by searching for requisition number R0086662. Complete an application with the following documents:

• Resume/Curriculum vitae
• Cover Letter describing research experience, interests, and career goals
• Names and contact information of three references

Upload all materials into the resume submission field. You can submit multiple documents into this one field or combine them into one PDF. Applications without all required documents will not receive full consideration.

Internal applicants: Search and apply for jobs on the UVA Internal Careers website.

For questions about the position, please contact Dr. Yaohua Yang at vta8we@virginia.edu.

For questions about the application process, please contact Bill Crane, Senior Recruiter, xer5ff@virginia.edu/

The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment.